README.md

---
license: mit
language:
- en
metrics:
- accuracy
- mse
- f1
base_model:
- dmis-lab/biobert-base-cased-v1.2
- google-bert/bert-base-cased
pipeline_tag: text-classification
model-index:
- name: bert-causation-rating-dr1
 results:
 - task:
 type: text-classification
 dataset:
 name: rating_dr1
 type: dataset
 metrics:
 - name: off by 1 accuracy
 type: accuracy
 value: 71.1864406779661
 - name: mean squared error for ordinal data
 type: mse
 value: 0.7796610169491526
 - name: weighted F1 score
 type: f1
 value: 0.7164302155262606
 - name: Kendall's tau coefficient
 type: Kendall's tau
 value: 0.8013637826548397
 source:
 name: Keling Wang
 url: https://github.com/Keling-Wang
datasets:
- kelingwang/causation_strength_rating
---

# Model description
This `bert-causation-rating-dr1` model is a fine-tuned [biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) model on a small set of manually annotated texts with causation labels. This model is tasked with classifying a sentence into different levels of strength of causation expressed in this sentence.
Before tuning on this dataset, the `biobert-base-cased-v1.2` model is fine-tuned on a dataset containing causation labels from a published paper. This model starts from pre-trained [`kelingwang/bert-causation-rating-pubmed`](https://huggingface.co/kelingwang/bert-causation-rating-pubmed). For more information please view the link and my [GitHub page](https://github.com/Keling-Wang/causation_rating).
The sentences in the dataset were rated independently by two researchers. This `dr1` version is tuned on the set of sentences with labels rated by Rater 1. 

# Intended use and limitations

This model is primarily used to rate for the strength of expressed causation in a sentence extracted from a clinical guideline in the field of diabetes mellitus management. 
This model predicts strength of causation (SoC) labels based on the text inputs as: 
 * -1: No correlation or variable relationships mentioned in the sentence.
 * 0: There is correlational relationships but not causation in the sentence.
 * 1: The sentence expresses weak causation.
 * 2: The sentence expresses moderate causation.
 * 3: The sentence expresses strong causation.
*NOTE:* The model output is five one-hot logits and will be 0-index based, and the labels will be 0 to 4. It is good to use [this `python` module](https://github.com/Keling-Wang/causation_rating/blob/main/tests/prediction_from_pretrained.py) if one wants to make predictions.

# Performance and hyperparameters
## Test metrics
This model achieves the following results on the test dataset. The test dataset is a 25% held-out stratified split of the entire dataset with `SEED=114514`.
 * Loss: 5.2014
 * Off-by-1 accuracy: 71.1864
 * Off-by-2 accuracy: 90.6780
 * MSE for ordinal data: 0.7797
 * Weighted F1: 0.7164
 * Kendall's Tau: 0.8014
 
This performance is achieved with the following hyperparameters:
 * Learning rate: 7.94278e-05
 * Weight decay: 0.111616
 * Warmup ratio: 0.301057
 * Power of polynomial learning rate scheduler: 2.619975
 * Power to the distance measure used in the loss function \alpha: 2.0

 
## Hyperparameter tuning metrics
During the Bayesian optimization procedure for hyperparameter tuning, this model achieves the best target metric (Off-by-1 accuracy) of *99.1147*, as the result from 4-fold cross-validation procedure based on best hyperparameters.

# Training settings

The following training configurations apply:
 * Pre-trained model: `kelingwang/bert-causation-rating-pubmed`
 * `seed`: 114514
 * `batch_size`: 128
 * `epoch`: 8
 * `max_length` in `torch.utils.data.Dataset`: 128
 * Loss function: the [OLL loss](https://aclanthology.org/2022.coling-1.407/) with a tunable hyperparameter \alpha (Power to the distance measure used in the loss function).
 * `lr`: 7.94278e-05
 * `weight_decay`: 0.111616
 * `warmup_ratio`: 0.301057
 * `lr_scheduler_type`: polynomial
 * `lr_scheduler_kwargs`: `{"power": 2.619975, "lr_end": 1e-8}`
 * Power to the distance measure used in the loss function \alpha: 2.0

# Framework versions and devices

This model is run on a NVIDIA P100 CPU provided by Kaggle. 
Framework versions are: 
 * python==3.10.14
 * cuda==12.4
 * NVIDIA-SMI==550.90.07
 * torch=2.4.0
 * transformers==4.45.1
 * scikit-learn==1.2.2
 * optuna==4.0.0
 * nlpaug==1.1.11